AIMC Topic: Infant

Clear Filters Showing 881 to 890 of 1049 articles

P22 Using VECTRA and AI analysis to monitor paediatric lesions: a review of cases.

The British journal of dermatology
BACKGROUND: Paediatric melanoma is a rare but important diagnosis. In the paediatric cohort, diagnostic challenges arise due to lesion variability and the inherent difficulties associated with paediatric assessment. Clinical decision-making is furthe...

Prediction of Poor Visual Outcomes at Idiopathic Intracranial Hypertension Diagnosis Using a Supervised Machine Learning Algorithm.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Idiopathic intracranial hypertension (IIH) is a vision-threatening disorder mainly affecting women of a reproductive age. Prompt diagnosis and intervention are vital to prevent vision loss, but validated tools to predict visual outcomes a...

Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.

Emerging microbes & infections
To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoencephalitis (STME) and to construct and validate predictive models using machine learning.Clinical data were collected from 100 cases of pediatric STM...

Utilization of a Digital Automated Cell Morphology Analyzer Results for Determining Differential White Blood Cell Counts in a Turkish Pediatric Population.

The journal of applied laboratory medicine
BACKGROUND: Manual morphological analysis of peripheral blood smears (PBS) with light microscopy is an essential diagnostic and monitoring tool. Recently, automated morphology analyzers have been developed that can preclassify cells using artificial ...

Machine learning using serial changes in proteinuria during initial steroid therapy to predict treatment response and immunosuppressant use in pediatric idiopathic nephrotic syndrome.

Clinical and experimental nephrology
BACKGROUND: Epidemiological studies on idiopathic nephrotic syndrome (INS) in children have identified no definitive factors predicting steroid-resistant nephrotic syndrome (SRNS) or frequent relapsing nephrotic syndrome. Research using machine learn...

Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays.

European radiology
OBJECTIVES: To evaluate how different test set sampling strategies-random selection and balanced sampling-affect the performance of artificial intelligence (AI) models in pediatric wrist fracture detection using radiographs, aiming to highlight the n...

History matters: Preventing severe allergic transfusion reactions.

American journal of clinical pathology
OBJECTIVE: Prior studies have shown that pretransfusion medication is not effective in preventing allergic transfusion reactions (ATRs), but these studies did not consider the patient's history of ATR. This study evaluated whether pretransfusion anti...

Accurate Paediatric Brain Tumour Classification Through Improved Quantitative Analysis of H MR Imaging and Spectroscopy.

NMR in biomedicine
Multimodality imaging is an emerging research topic in neuro-oncology for its potential of being able to demonstrate tumours in a more comprehensive manner. Diffusion-weighted magnetic resonance imaging (dMRI) and proton magnetic resonance spectrosco...

Dosing prediction of valproic acid in pediatric patients with epilepsy: population pharmacokinetic model or machine learning model?

European journal of clinical pharmacology
PURPOSE: This study develops and compares population pharmacokinetics (PopPK) models and machine learning methods, including neural networks, to predict steady-state trough concentrations in pediatric patients and provide improved dosing recommendati...

Classification of epilepsy seizure types in pediatrics based on Turkish EEG reports.

Epilepsy research
This study focuses on the binary classification of pediatric epilepsy seizure types as focal or generalized using Turkish electroencephalography (EEG) reports, leveraging natural language processing (NLP) and machine learning methodologies. A novel d...